iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas
iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas
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iBILL：使用 iBeacon 和惯性传感器在大型开放区域进行准确的室内定位
DOI：
10.1109/access.2017.2726088
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发表时间：
2017-01-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Xinbing
中科院分区：
文献类型：
--
作者：
Wu, Xudong;Shen, Ruofei;Wang, Xinbing
室内定位技术作为基于位置服务（LBS）的关键技术之一，受到了研究和产业界的广泛关注。尽管使用智能手机惯性传感器进行定位做出了巨大的努力，但由于用户移动性和磁场波动的不确定性所产生的累积误差，其性能在大型开放区域（如霍尔斯，超市和博物馆）仍然不令人满意。关于这一点，本文介绍了iBILL，这是一种在大型开放区域联合使用iBeacon和惯性传感器的室内定位方法。利用惯性传感器通过改进的粒子滤波器估计用户的实时位置，对增广粒子滤波算法进行修正，以科普磁场波动的影响。当用户进入iBeacon设备集群的附近时，他们的位置基于iBeacon设备的接收信号强度被准确地确定，并且因此可以校正累积的误差。由Apple Inc.为了开发LBS市场，iBeacon是一种低功耗的蓝牙，我们描述了使用它时本地化的优点和局限性。此外，在iBeacon设备的帮助下，我们还提供了两个定位问题的解决方案，这些问题由于计算开销越来越大和任意放置的智能手机而长期以来一直很坚韧。通过在我们校园的图书馆进行广泛的实验，我们证明了iBILL在3.5米的大面积开放区域内显示出90%的误差。
As a key technology that is widely adopted in location-based services (LBS), indoor localization has received considerable attention in both research and industrial areas. Despite the huge efforts made for localization using smartphone inertial sensors, its performance is still unsatisfactory in large open areas, such as halls, supermarkets, and museums, due to accumulated errors arising from the uncertainty of users’ mobility and fluctuations of magnetic field. Regarding that, this paper presents iBILL, an indoor localization approach that jointly uses iBeacon and inertial sensors in large open areas. With users’ real-time locations estimated by inertial sensors through an improved particle filter, we revise the algorithm of augmented particle filter to cope with fluctuations of magnetic field. When users enter vicinity of iBeacon devices clusters, their locations are accurately determined based on received signal strength of iBeacon devices, and accumulated errors can, therefore, be corrected. Proposed by Apple Inc. for developing LBS market, iBeacon is a type of Bluetooth low energy, and we characterize both the advantages and limitations of localization when it is utilized. Moreover, with the help of iBeacon devices, we also provide solutions of two localization problems that have long remained tough due to the increasingly large computational overhead and arbitrarily placed smartphones. Through extensive experiments in the library on our campus, we demonstrate that iBILL exhibits 90% errors within 3.5 m in large open areas.